Pratique du Shadowing: SELECT COUNT (*) can impact your Backend Application Performance, here is why - Apprendre l'anglais à l'oral avec la vidéo
Création de la leçon...
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aggregating large entries in the database to perform a count for
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example is a lot of work the database has to sort
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through large number of records whether it's in an index
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or in the raw heap table itself doing this too often can impact the performance of both your database
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and your application let us discuss why count can be slow and an alternative if you really don't want the actual count, but yes, you want an estimate.
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Let's just jump into it.
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All right, guys.
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So there are many ways you can execute a count on your data.
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So I have here a grades table, our famous student grades table.
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So there is a G, which the grade itself.
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That's the ID of the students, right?
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And I think it has like around 60 million rows that I created.
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So what I'm going to do here is do a select count G from grades,
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where id between 1000 and 4000 give me the grades of those people
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but i just want to count if you execute that it's
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so fast right now don't pay attention to the speed
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because i have caching going on i executed this query many times
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but i want to pay attention to the number that come back
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2900 and the reason this number is a little bit low from comparing 2000
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and 4000 which around should be around 3000
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because count g will return entries that are not null
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and just just think about it i have an index on the id field
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that means the database have used
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that index to pull the rows in order to count them
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so we're on the index but we asked the database to do a count on g
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that means we had to go to the table to check those uh the value of g whether it's null
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or not So let's do an explain.
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Let's just add an explain analyze before these puppies.
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Let's see what happened here.
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Why do that?
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Let's pay attention to what happened here.
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We're doing an index scan on Postgres.
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We have returned 3001 rows because guess what?
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We're in the index.
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The actual index entries, I don't have any deleted rows by the way here.
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So 3001 is about right, but the final count has been reduced
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because we went back to the table to get the value G because that's what we asked it.
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We ask, hey, count the value G.
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And when you do count G here or count any field, the database will filter through the fields and only count the not null values.
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And I have few nulls there.
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Okay.
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So now it uses an index scan.
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And that tells me that, hey, I scan the index
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but i had to go to the table it's not an index only scan right let's spice things up
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and see do the same thing here
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but i'm going to do a select star this time
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and a lot of people have a misconception
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that count star actually goes to the table and fetch all the fields
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and count the fields no almost no database do this anymore
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right count star essentially means just count whatever entries you have right
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this will include null values if you're scanning the index give me the values right
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and you can see that we got a higher number
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so let's take a look at the plan that did
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that postgres used to do that stuff what did you use postgres tell me
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if you will look at the plan now look at this it's It's an index only scan
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and always index only scan always trumps
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and always it's better than the actual index scan because I don't really need to go back to the table.
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Again, don't pay attention to these numbers because I have caches all over the place.
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I just want you to, the most important thing is to understand the plan.
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Running these numbers don't mean anything right now because first of all, I'm in a container.
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I have a large amount of memory, so the database will start caching these pages if I execute them over and over again.
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But just understand that plan is the most important thing.
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And as a result, the larger the number of rows come back, the more work the database is doing.
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So 118 milliseconds.
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Index only scan.
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All right, Jose, what are you trying to do here?
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Here's what I'm going to do.
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I'm going to do an update now.
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I'm going to do an update grades.
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Set g equal 20, where id is between 1,000 and 4,000.
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So those rows between 1,000 and 4,000, I'm going to slam all of them and update them.
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Change this.
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Let's see how the Postgres will freak out now.
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What will happen?
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I'm going to execute this count star, and then let's see what will happen.
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all of a sudden guys look this number jumped again not by much
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but it is significant the more rows the more actual real data you have this will go back
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but but look at that it still says index only scan
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but look at this heap features i want you to pay
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attention to this the moment you start seeing heap features that means index only scan yeah we did only scan the index,
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but we had to go back to the table 6,002 times, right?
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For this amount of values, right?
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I'm not sure these are the blocks or the actual rows.
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I have to go back and check.
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But we had to go back this amount of time, which is expensive, right?
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Why?
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because we have updated the values the visibility map told the index scanner
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that hey by the way yeah you're scanning the index
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and i have i'm going to give you only values in the index which is
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usually fast again if you're not scanning the whole index
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but these rows that i'm scanning in the index might have been updated might have been deleted
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so i have to go back to the heap
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where the actual visibility of the row exists to check if the row is actually deleted.
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Because when you delete something in Postgres, the index is not immediately marked as deleted.
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It just adds a new record and keeps the old records for MVCC reasons,
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so multiple concurrency control, so other transactions can't see those old tuples.
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All right.
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So how do we fall?
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How do you solve this problem?
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Very simple.
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You just vacuum the table.
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Vacuuming the table will update the visibility map saying that, by the way, those old rows that you just updated, nobody's reading them.
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Nope.
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There are no running transactions that are reading them.
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In a production system, there might be, but not now.
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So if I do now the same query again, so if we do it again, you can see that we got heap features zero.
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All right.
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And guys, every time I increase that number 3004, you can see that this is going to get slower and slower and slower.
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Just to show you that, for example, now it took one and a half second to execute.
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And then if I increase that number a little bit, you can see, oh, you can feel it.
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So count is not cheap, right?
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It was cheap for the 3,000 rows that I'm going to return.
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But every time you do that, the plan now changes.
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It says, okay, we're still going to do an index, but I'm going to use threading.
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The database decided to do multiple threads, multiple workers, not necessarily threads, multiple workers to scan that index.
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So I can give you the results, right?
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Still, we're good we didn't do any heap features
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but it took six seconds to return this many rows right so every time you increase that number goes larger
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the operation is going to go slower.
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It's just proportional.
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It's just so proportional.
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So all right.
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So what if I, I'm saying I don't care about the actual row count, right?
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I don't really care about giving the actual exact value, right?
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But here's what I want to do.
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I want you just to give me an estimate.
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If you do just an explain, right?
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And let's just format this so it's JSON-y, right?
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If you do that, without an analyze again, without an analyze, analyze actually execute the query.
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Explain will not execute the query, but it will estimate it.
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It will estimate that, hey, I'm going to use the index.
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I am planning that I might get this much rows back, 2,868, right?
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So compared to the select count, so it's not an accurate number.
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It's 200 values up.
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And a lot of people use this.
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And I actually got this trick from a blog that I'm going to reference below.
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Let's remember that the table itself has some statistics to update itself.
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So Postgres, without actually looking at the table, it knows roughly how many rows are on the table.
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roughly how much rows will come back are this are these actual correct numbers absolutely not
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but if you're building instagram or you're building something
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that like count the likes or something like
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that this is way better right so i can quickly estimate this stuff
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and as you update your table obviously a good idea to do an analyze on your grades table
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or your table that will update the statistics to the correct numbers
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and obviously this operation is going to take a long time all right guys that's it for me today
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that was count and how count is essentially a lot of work for the database
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and if you really don't need an actual count correct number especially
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if it's in the millions why would you show the the user 60 million
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and 320 exact number right almost no one does that right
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and let's see yeah so it's on estimate always matter
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if you're working with a fewer rows you can do an actual account but
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if you expect the table to grow avoid you using an
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excellent account use do an estimate with a planner like that
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if that works for you hope
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that that's way more than enough all right again guys i'm
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gonna see you in the next one you guys stay awesome goodbye
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « SELECT COUNT (*) can impact your Backend Application Performance, here is why ». Les mots qui reviennent le plus souvent : index, count, row, table, scan. Cette vidéo contient 156 phrases et 1723 mots à répéter en shadowing. La partie parlée dure 10:37. Le locuteur parle à un rythme naturel d’environ 162 mots par minute, proche d’une conversation courante. 86 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| scan verbe | /skæn/ | scanner, fouiller |
| execute verbe | /ˈɛksɪˌkjuːt/ | exécuter, mettre à mort |
| analyze verbe | /ˈæn.əˌlaɪz/ | analyser |
| query nom | /ˈkwɪɹ.i/ | question |
| cache nom | /kæʃ/ | cache, mémoire cache |
| visibility nom | /ˌvɪz.əˈbɪl.ɪ.ti/ | visibilité |
| proportional adjectif | proportionnel | |
| vacuum nom | /ˈvæ.kjuːm/ | vide, vacuum |
| millisecond nom | /ˈmɪlɪˌsɛkənd/ | milliseconde |
| misconception nom | /ˌmɪs.kənˈsɛp.ʃən/ | idée erronée |
| planner nom | /ˈplænɚ/ | agenda |
| scanner nom | /ˈskænə/ | scanneur, scanner |
| fetch verbe | /fɛt͡ʃ/ | ramener, rapporter |
| aggregate nom | /ˈæɡ.ɹɪ.ɡət/ | agrégat |
| spouse nom | /spaʊ̯s/ | époux, épouse |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| come back verbe | revenir |
| go back verbe | rentrer, retourner |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- Let's just jump into it.
- Let's just add an explain analyze before these puppies.
- Let's see what happened here.
- Let's pay attention to what happened here.
- How do you solve this problem?
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have returned · has been reduced · have updated |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | has been reduced · have been updated · is actually deleted |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | time, which is · index which is |
Prononciation à surveiller
Le locuteur utilise 40 contractions et formes réduites, comme I'm, don't, you're. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: cache /kæʃ/, misconception /ˌmɪs.kənˈsɛp.ʃən/
- Mots longs — placez bien l’accent: visibility /ˌvɪz.əˈbɪl.ɪ.ti/, concurrency /kəŋˈkʌɹənsi/, millisecond /ˈmɪlɪˌsɛkənd/, misconception /ˌmɪs.kənˈsɛp.ʃən/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme scan, execute, analyze.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.